Preslav Nakov, Isabelle Augenstein, Momchil Hardalov, Arnav Arora, Checkstep Research
We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.
Stance detection concerns the classification of a writer's viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect to a claim vs. an (implicit) topic. Moreover, task definitions vary, which includes the label inventory, the data collection, and the annotation protocol. All these aspects hinder cross-domain studies, as they require changes to standard domain adaptation approaches. In this paper, we perform an in-depth analysis of 16 stance detection datasets, and we explore the possibility for cross-domain learning from them. Moreover, we propose an end-to-end unsupervised framework for outof-domain prediction of unseen, user-defined labels. In particular, we combine domain adaptation techniques such as mixture of experts and domain-adversarial training with label embeddings, and we demonstrate sizable performance gains over strong baselines, both (i) indomain, i.e., for seen targets, and (ii) out-ofdomain, i.e., for unseen targets. Finally, we perform an exhaustive analysis of the crossdomain results, and we highlight the important factors influencing the model performance.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2104.07467")
get_code_for_paper("2104.07467")
have("2104.07467")
Connect an agent — have() is free.